arXiv:2606.30778v1 Announce Type: new Abstract: Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalysis to combustion to the origin of life. Constructing such a reaction network for a given chemistry has been impractical: it requires finding and characterizing tens of thousands of TS, a task for which traditional methods such as density functional theory (DFT) are typically prohibitively slow and require reactant and product as input. We introduce ReactionAtlas, which
The development of advanced machine learning techniques, particularly in graph neural networks and generative AI, is enabling breakthroughs in complex scientific simulations and discovery.
This development significantly accelerates the mapping and understanding of chemical reaction networks, which are fundamental to advancements in chemistry, materials science, and energy.
Traditional, prohibitively slow methods for mapping chemical reactions are being supplanted by AI-driven approaches, making comprehensive network exploration feasible for the first time.
- · AI/ML researchers
- · Pharmaceutical industry
- · Catalysis research
- · Materials science
- · Traditional computational chemistry methods
Faster discovery of novel chemical processes and materials.
Accelerated development of new drugs, catalysts, and energy solutions due to improved chemical understanding.
Potential for autonomous chemical discovery systems, vastly speeding up scientific R&D cycles across multiple industries.
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